Top Generative AI Interview Question with Answers

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As organisations adopt Artificial Intelligence, there is a huge demand for AI Engineers and Consultants. According to LinkedIn, there are 7000+ GenAI job openings in Bengaluru and 134000+ worldwide. GenAI professionals are paid very well. Someone starting can get a pay ranging between INR 8LPA and 43LPA. Product-based companies, especially startups, pay GenAI professionals an average of INR 30 LPA—INR 40 LPA.

• Generative AI Interview Questions for Freshers
• Generative AI Interview Questions for Intermediate
• Generative AI Interview Questions for Experienced

1. What is the difference between discriminative and generative models in machine learning?

The difference between both models lies in their workings. Let’s have a look at some major differences. On one hand, discriminative models are focused on distinguishing data. They work on the principle of supervised machine learning, whereas generative models focus on generating new data by learning from previous ones. The model first learns the pattern and then generates the new data instance. However, these models have far more complex training than discriminative models.

2. What is the difference between correlation and causation?

Correlation can be defined as the relationship between two things, but the twist is they can be either related or unrelated. For example, Sales of winter clothes and falling sick in winter are correlated (as they both happen in winter), but sales of winter clothes don’t impact health.

Causation can be defined as when one thing directly impacts another. Here, we are working with cause-and-effect relationships. For instance, eating stale food will result in a person falling sick. That’s causation

3. What is the significance of tokenization in LLM processing?

Tokenization refers to breaking down textual data into smaller pieces termed tokens. This helps the model understand and process the data more easily and effectively.

Here are some detailed points that will help you understand the same:

• Since computers can’t understand everything, hence tokenization helps them to understand data more easily by breaking down the words
• Like, “I love Data Science” will be broken into “I”, “love”, “Data”, and “Science”

4. What is Narrow AI? Mention a few of its typical applications.

These are the types of AI systems capable of handling only one task. They are the most basic and simple AI systems, and due to their limitation of handling one or a few tasks, they are termed Narrow AI. The best example of this is Siri.

5. Explain how natural language processing (NLP) works in terms of Generative AI.

Machines don’t generally understand human language, so they try to understand the pattern in the textual data. For this, they break down the textual data into smaller pieces of text. This breaking down helps the generative models learn the structure of the words, which words can appear together, and how different topics can be modelled. Once the model is trained on this data, it can predict what comes next based on those broken tokens and an understanding of how they can be related together.

6. What are the most common use cases of generative AI?

Generative AI is being used in the fields, including

• Image Generation, wherein we can generate relevant and realistic images using Image Models like DALLE3 & ImaGen3.
• Text Generation is a common use case of generative AI that is mostly used in chatbots, text summarization, and machine translation.

7. What do you understand about latent space in the context of VAEs?

Latent Space is a compressed representation of data that only has the important feature in it. In VAEs, data compression is done to reduce the complexity of data by focusing on the important details and ignoring the irrelevant ones. In this way, we can capture more information in less space, and this space is termed Latent Space. It’s like removing seeds from a watermelon.

8. What is Mode Collapse in GANs?

In ideal situations, the generative models should be able to generate a variety of data every time, like if we are working with animal data, so it should generate dogs sometimes, cats. But sometimes, the model starts to generate the same kind of results over and over again, no matter how many times we ask for it. In this case, the model becomes lazy. This happens because the model can find a way to fool the system (discriminator), and hence it continues to generate the same data. This is called Mode collapse.

9. Why is training GANs a challenge?

Training of GANs is complex due to multiple reasons, a few of which can be found listed below:

• One of the major issues is when the machine starts to generate similar kinds of samples, also known as mode collapse, which limits the creativity of the model
• The second issue with the competition between the discriminator and generator is that if anyone between them becomes stronger, the results will be messed up.
• Hyperparameters are another area wherein a small change can result in an enormous change in the final results.
• Moreover, it’s very hard to judge how a GAN is performing because there isn’t a perfect way or metrics to evaluate the performance.

10. What is hallucination, and how can it be managed with Prompt Engineering?

When a user queries something to a language, and in response, the language model reverts the user with information that is incorrect, inappropriate or made-up but presents as factual information. This is termed as Hallucination.

Using correct prompt techniques, hallucinations can be eliminated:

• If users are specific about their needs and if a detailed description has been provided. For e.g., “Give 10 facts about the life cycle of plants.”
• Moreover, we can also request the model to provide us with specific URLs
• Also, providing the context or background of the scenario is a better way to eliminate hallucinations.
• Additionally, breaking down the instructions can help improve the quality of outputs.

11. How do you determine the quality of generated samples from a generative model?

The quality of the generated data instances can be checked through multiple metrics. Some of the most commonly used practices comprise:

• Human Evaluation—Humans are one of the most reliable measures of judgement, as they can judge any output much better on different scales like creativity, realism, or relevancy. But sometimes, they can also be biased.
• Pixel-based Evaluation – In this technique, the generated images are directly compared to real ones and metrics like Mean Squared Error or Structural Similarity Index (SSI). It’s very reliable but is a cost-consuming process.
• Feature-based Evaluation – In this method, we use a pre-trained model to analyze the feature more deeply by judging how close the outputs are to real data or how consistent they are throughout.

12. How can one resolve the issue of bias in the Large Generative Models?

Language models can always have the issue of bias since they are trained on data that often comes from different data sources. To tackle these issues, we can follow these tricks:

• The most suitable way is to reduce the bias from the data itself. This will help the model to generate a more suitable output
• Human oversight can be a crucial step since humans are better at judging the outputs generated by the model.
• Following ethical guidelines can also be useful since a model that is trained ethically is less likely to be biased.

13. How can convergence and scalability be handled with large-scale generative models?

By adequately managing convergence, we can ensure that the model learns the data properly and can perform efficiently. This can be done in multiple ways, which might include:

• Tuning the learning rate can help the model learn more effectively
• Using tricks like gradient clipping and normalization can help the model generalise the data well.

Scalability can be understood as making the model capable of handling large datasets and ensuring it works the same when the model becomes complex. This can be achieved using:

• Implementing distributed and parallel computing so that workload can be split across multiple devices
• Eliminating unnecessary gradients to make the model less complex

14. What is the role of transformers in Generative AI?

Compared to older models, like LSTMs or RNNs, transformers work on all the data at once, which, in a way, accelerates the generation process. It starts with focusing on the most important words (Attention Mechanism). Once we have found the important words, we try to find the relationship between those words and understand their context. This helps the transformer be one of the most suitable architectures in Generative Models.

15. What is “in-context learning” in Large Language Models?

Let’s say you are trying to solve a question, so if we give some examples to the model, it can learn from them. Then it can generate the responses based on that learning. This is what “in-context” learning is. In this technique, the model tries to learn from the input provided by the user itself, rather than learning from any additional training.

16. How do you evaluate the performance of a generative model, and what metrics are commonly used?

Since the generative models work completely on the probabilistic approach for generating new samples, hence the evaluation metrics for the generative models are a bit different, like:

• Inception Score (IS)—This score helps us understand how realistic the generated sample output is compared to the original data. It measures two important aspects, which include Diversity and Sharpness of the output. If both of them are satisfied, then there is a high IS Score; otherwise, the IS score remains lower.
• Frechet Inception Distance (FID) – The major difference between IS and FID is that IS doesn’t consider the distribution of real data. This results in the measurement of similarity in data samples and the test data.
• CLIP Score—This technique calculates the visual semantics between the text provided and the image thus created by manipulation. It does this by finding the cosine similarity between the embeddings.

17. How do you implement and tune the loss functions for generative models, and why is this important?

Working with the loss function in generative models results in more effective learning and high-quality results. The loss functions and their tuning might vary for different generative models. For GANs and VAEs, for example, we require loss functions that can quantify the difference between the generated data and the original data.

• While performing these operations, we can use a loss function variation to find the loss, which might comprise Mean Squared Errors, Binary Cross Entropy, or Kullback-Leibler Divergence.
• Now, depending on the models, we can also modify the loss function according to our needs. For VAEs, KL divergence is mostly combined with reconstruction loss to ensure the latent space is regularized.

Additionally, for tuning the model, we can work with the learning rate, size of the batch taken and the number of epochs so that the loss can be optimized. Moreover, regularisation techniques can also help tuning the model while implementing proper data augmentation techniques.

18. How do you ensure your AI models are ethical and unbiased?

When we talk about AI & ML, the biases aren’t just due to human errors. They can also happen due to the distortions caused by machine learning models. Here are a few points that can be taken into consideration to mitigate the same:

• To get different data: The training data set is not biased on negative stereotypes as it includes various opinions and communities. Thus, it is recommended to introduce balanced datasets with different populations, cultures, and situations.
• Bias Detection and Mitigation compare models with their regular predictions based on gender, race, or socioeconomic status bias. Tools and methodologies such as fair constraints and adversarial debiasing may reduce biases caused by training.
• Explainability and transparency: The explanation methods that implement the explanation strategies must assist stakeholders in understanding how an AI model concludes. If performed by AI, it will look for any ethical issues that may be present and provide an audit trail of decision-making if necessary.
• The monitoring phase is a continuous process consisting of periodic reviews. It deploys monitoring tools designed to prevent models from having harm or biases against people from certain groups unintentionally.

19. How can we supervise the behaviour and nature of content generated using Generative AI models?

This can be achieved by multiple ways, which may comprise of:

• Human Engagement – This process is very reliable in context with the issues, since as humans, we can rate or judge the generated content on numerous parameters
• Prompt Engineering can be defined as reviewing and refining the prompt (textual inputs) given to the model. Eventually, any Language Model can generate the results based on what has been asked, so if it’s garbage in, it’s garbage out.
• Model Evaluation: We can regularly evaluate the model’s performance using different metrics and automated testing frameworks
• Data Quality Assurance: The quality of the data is indeed one of the most important criteria for judging the content thus generated.

20. What is the concept of Diffusion Models, and how are they different from GANs or VAEs?

Diffusion is a process in which we first start with a clear picture and then gradually we add some noise to the data till it becomes a complete blur. This is often termed a forward process. After this, we gradually remove the noise while the model learns about the data. This process is termed a reverse process. Using this reverse process ,the model learns more about the data. Once the model has completely learnt, then it can create new images.

If we had to compare Diffusion models with VAEs and GANs, this information might help us.